904 resultados para arrival
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Mode of access: Internet.
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Mode of access: Internet.
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Trial before the House of lords, August-November, 1820.
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23763
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2 scans made - 1of1 includes caption pasted below, 2of2 = image only
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Based on Joseph Strutt's "Manners and Customs". For the use of children.
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Microfilm. Ann Arbor, Mich., University Microfilms [n.d.] (American culture series, Reel 498.8)
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Mode of access: Internet.
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Purpose. Drivers adopt smaller safety margins when pulling out in front of motorcycles compared with cars. This could partly account for why the most common motorcycle/car accident involves a car violating a motorcyclist's right of way. One possible explanation is the size-arrival effect in which smaller objects are perceived to arrive later than larger objects. That is, drivers may estimate the time to arrival of motorcycles to be later than cars because motorcycles are smaller. Methods. We investigated arrival time judgments using a temporal occlusion paradigm. Drivers recruited from the student population (n = 28 and n = 33) saw video footage of oncoming vehicles and had to press a response button when they judged that vehicles would reach them. Results. In experiment 1, the time to arrival of motorcycles was estimated to be significantly later than larger vehicles (a car and a van) for different approach speeds and viewing times. In experiment 2, we investigated an alternative explanation to the size-arrival effect: that the smaller size of motorcycles places them below the threshold needed for observers to make an accurate time to arrival judgment using tau. We found that the motorcycle/car difference in arrival time estimates was maintained for very short occlusion durations when tau could be estimated for both motorcycles and cars. Conclusions. Results are consistent with the size-arrival effect and are inconsistent with the tau threshold explanation. Drivers estimate motorcycles will reach them later than cars across a range of conditions. This could have safety implications.
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Besides researching the last goal of the execution of a trial between Francisco Pizarro, governor of Peru, and his mandatory, Pedro de Barrantes on behalf of the gold sent to Spain, the present work reviews all the data found in the Archives of the Grenade Chancellery on the arrival of gold to Spain. Also, a curious trial on a subject of Preste Juan is included.
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Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.
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This article presents a panorama of the area of the linguistics of the indigenous languages in Brazil within the discipline of Brazilian linguiistics as a whole. Special attention is given to those aspects related to its specific development. It is argued that in contrast to what is commonly supposed, the arrival of the Summer Institute of Linguistics (1959) not only was not the beginning of this area of study in the country, but it even contributed to the delay in its establishment. It was only after the return of Brazilian scholars educated abroad who were interested in the study of the national indigenous languages that a specialized branch of linguistics directed to the study of these languages began to take form. The present situation of the area and perspectives for future development are both explored.